Asthma self-management app for Indonesian asthmatics: A patient-centered design

Ghozali, Muhammad Thesa; Satibi, Satibi; Ikawati, Zullies; Lazuardi, Lutfan

doi:10.1016/j.cmpb.2021.106392

Cited by 18 publications

(14 citation statements)

References 17 publications

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“…We select Disease as the experimental sensitive attribute. The Disease attribute contains ten values, randomly generating disease types and using sensitivity weights to measure sensitivity [ 16 ]. The larger the value, the higher the sensitivity, as shown in Table 6 .…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms

Khan

Saravanan²,

Gnanaprakasam

et al. 2022

Computational Intelligence and Neuroscience

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With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm’s execution time.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms

Khan

Saravanan²,

Gnanaprakasam

et al. 2022

Computational Intelligence and Neuroscience

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“…To improve the model's generalization ability, the idea of reference [ 13 ] is used to make a training sample set by sliding window, and the original signal is overlapped to enhance the data set. Selecting the sliding window size as 3000 and the offset as 300, 16 data sets with different loads and states can produce 10,388 data sets for experiments, including 8,656 training sets and 1732 test sets, and the ratio is 5 : 1.…”

Section: Experimental Setup and Results Analysismentioning

confidence: 99%

“…Since it does not require preprocessing for feature extraction, the original input image can be directly output, so it is widely used. This processing mode is called an “end-to-end” processing mode [ 13 , 14 ]. A schematic diagram of a simple CNN structure is shown in Figure 1 .…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Improved Capsule Network Optimization Hierarchical Convolution Algorithm for Mental Health Recognition

Bhowmik¹,

Bhusnurmath

Sahu

et al. 2022

Computational and Mathematical Methods in Medicine

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To address the shortcomings of standard convolutional neural networks (CNNs), the model structure is complex, the training period is lengthy, and the data processing technique is single. A modified capsule network is presented to optimize hierarchical convolution—the algorithm for identifying mental health conditions. To begin, two types of data processing are performed on the original vibration data: wavelet noise reduction and wavelet packet noise reduction; this retains more valuable information for mental health identification in the original signal; secondly, the CNN employs the concept of hierarchical convolution, and three distinct scaled convolution kernels are utilized to extract features from numerous angles; ultimately, the convolution kernel’s extracted features are fed into the pruning strategy’s capsule network for mental health diagnosis. The enhanced capsule network has the potential to significantly speed up mental health identification while maintaining accuracy. It is time to address the issue of the CNN structure being too complex and the recognition impact being inadequate. The experimental findings indicate that the suggested algorithm achieves a high level of recognition accuracy while consuming a small amount of time.

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“…However, psychology believes that when a person tries to hide his genuine emotions, occasionally, there are emotions that leak out. Psychological behaviors were first discovered in 1966 [ 20 ]. Three years later, [ 21 ] used the term psychological behaviors when analyzing a video interview of a patient who attempted suicide.…”

Section: Introductionmentioning

confidence: 99%

“…In subsequent experiments, they found that the subjects' psychological-expression recognition ability was positively correlated with lie recognition ability [ 13 ]. Afterwards, the Japanese and Caucasian Brief Affect Recognition Test (JACBART) [ 14 , 15 ] was conducted, verifying that the subjects' psychological-expression recognition ability was positively correlated with lie recognition ability [ 20 ], it can be proved that psychological-expressions can effectively help people identify lies. Face psychological expression recognition involves image processing and analysis, computer vision, artificial intelligence, psychology, biology, and other directions.…”

Section: Introductionmentioning

confidence: 99%

Homogeneous Decision Community Extraction Based on End-User Mental Behavior on Social Media

Gupta

Kumar

Bangare

et al. 2022

Computational Intelligence and Neuroscience

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Aiming at the inadequacy of the group decision-making method with the current attribute value as interval language information, an interval binary semantic decision-making method is proposed, which considers the decision maker’s psychological behavior. The scope of this research is that this paper is based on localized amplification method. The localized amplification method used in this research may amplify physiological movement after removing unwanted noise, allowing the movement trend to be seen with the naked eye, improving the CNN network’s mental identification accuracy. These two algorithms analyze the input picture from various perspectives, allowing the CNN network to extract more information and enhance identification accuracy. A new distance formula with interval binary semantics closer to decision-makers thinking habits is defined; time degree is introduced. An optimization model is established to solve the time series weights by considering the comprehensive consistency of expert evaluation. Based on prospect theory, a prospect deviation value is constructed and minimized weight optimization model, using the interactive multiple attribute decision community making (TODIM) method based on the new distance measure to calculate the total overall dominance of the schemes to rank the schemes. Taking the selection and evaluation of supply chain collaboration partners as an example, the effectiveness and rationality of the proposed method are verified.

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Asthma self-management app for Indonesian asthmatics: A patient-centered design

Cited by 18 publications

References 17 publications

Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms

Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms

Improved Capsule Network Optimization Hierarchical Convolution Algorithm for Mental Health Recognition

Homogeneous Decision Community Extraction Based on End-User Mental Behavior on Social Media

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